Discriminating Tuberculous Pleural Effusion from Malignant Pleural Effusion Based on Routine Pleural Fluid Biomarkers, Using Mathematical Methods.

Background
The differential diagnosis of tuberculous pleural effusion (TPE) and malignant pleural effusion (MPE) is difficult because the biochemical profiles are similar. The present study aimed to differentiate TPE from MPE, using a decision tree and a weighted sparse representation-based classification (WSRC) method, based on the best combination of routine pleural effusion fluid biomarkers.


Materials and Methods
The routine biomarkers of pleural fluid, including differential cell count, lactate dehydrogenase (LDH), protein, glucose and adenosine deaminase (ADA), were measured in 236 patients (100 with TPE and 136 with MPE). A Sequential Forward Selection (SFS) algorithm was employed to obtain the best combination of parameters for the classification of pleural effusions. Moreover, WSRC was compared to the standard sparse representation-based classification (SRC) and the Support Vector Machine (SVM) methods for classification accuracy.


Results
ADA provided the highest diagnostic performance in differentiating TPE from MPE, with 91.91% sensitivity and 74.0% specificity. The best combination of parameters for discriminating TPE from MPE included age, ADA, polynuclear leukocytes and lymphocytes. WSRC outperformed the SRC and SVM methods, with an area under the curve of 0.877, sensitivity of 93.38%, and specificity of 82.0%. The generated flowchart of the decision tree demonstrated 87.2% accuracy for discriminating TPE from MPE.


Conclusion
This study indicates that a decision tree and a WSRC are novel, noninvasive, and inexpensive methods, which can be useful in discriminating between TPE and MPE, based on the combination of routine pleural fluid biomarkers.


INTRODUCTION
Pleural effusion is a common complication estimated to affect more than 400 people per 100,000 (1). There are two types of pleural effusion, namely transudative and exudative. A transudative pleural effusion develops when the permeability of the capillaries in the lung is altered.
Exudative pleural effusion reflects the presence of primary pleural disease and requires etiological investigation (2). 50% of all the exudates (3,4). However, malignant (MPE) and tuberculous pleural effusion (TPE) have similar biochemical profiles and distinguishing between them can be difficult (3,4). In both types, the pleural fluid is generally lymphocytic, with a predominance of T lymphocytes, particularly CD4-positive T cells (5). Since treatments vary noticeably, a rapid and accurate differential diagnosis is necessary.
Conventional methods, such as thoracentesis and analysis of pleural fluid cytology, histological analysis of tissue obtained via surgical biopsy, image-guided biopsy and local anesthetic thoracoscopy, are not always helpful as they have limitations (2,(6)(7)(8). Cytological examinations of pleural fluid can help in diagnosis of 66% of definite cases of malignancy (9). Pleural fluid cultures are positive for mycobacteria in up to 20% of cases and the waiting time for culture results is approximately 1 month (6).
A combination of the cytological method and biopsy can increase the rate of diagnosis to 73% (9). Even though pleuroscopy could determine the cause of pleural effusion in these patients with 95% accuracy, this facility is invasive and not available in most hospitals (10,11). Therefore, developing a less-invasive, accessible and early method with high accuracy is greatly needed for diagnosing the causes of pleural effusions.
Previous studies have reported the performance of various biomarkers, such as nucleated cells, lymphocytes, neutrophils, eosinophils, cholesterol, proteins, lactate dehydrogenase (LDH), adenosine deaminase (ADA), interleukin-6 and tumor necrosis factor-α, to differentiate between MPE and TPE (12)(13)(14). However, most of these investigations are based on each marker separately, and should be interpreted alongside clinical findings and with the results of other conventional tests (13,14). It appears that a combination of biological markers can increase the accuracy of diagnosis (12,13).
Various classification models have been constructed for differentiating between diseases. Sparse representationbased classification (SRC) is a new and powerful data processing method that has shown good performance in the classification of diseases (15)(16)(17)(18). In this study, we propose a weighted sparse representation-based classification (WSRC) method, which is a modified version of SRC. WSRC improves the classification accuracy of the system through adding the weights (17).
Making the right decision plays an important role in diagnostic medicine. A decision tree is an effective and reliable supporting tool for decision-making that provides an accurate classification through the use of simple representation of the information gathered. This model consists of starting points (tests or clinical questions) and branches which represent the alternative outcomes of each test or question (19).
The aim of the present study was to differentiate between TPE and MPE using a decision tree and a WSRC method, based on the best combination of routine pleural fluid biomarkers. Moreover, WSRC is compared with the conventional classification methods in terms of classification accuracy.

Data collection
In this research, we undertook a retrospective study of 236 patients with a diagnosis of pleural effusion due to tuberculosis (n=100) or cancer (n=136) who were admitted At the time of admission and before any medical treatment was considered, pleural fluid was analyzed in terms of differential cell count, LDH, protein, glucose and ADA levels. Biochemical measurements were performed using standardized photometric methods (Hitachi models 717,917 or modular DP, Roche Diagnostics Mannheim Germany) and manual microscopy was used for the cell count. Pleural ADA was measured using an automated ultraviolet kinetic test (Roche diagnostic, Barcelona, Spain).

Sparse Representation-based Classification (SRC)
A SRC classification approach assigns sample vector as an input, which belongs to an unknown class. This approach is extended to SRC when vector is being assigned to the class that is represented with training samples and is related to coefficients of sparse representation of in the most efficient way (15,(20)(21)(22).

Weighted sparse representation-based classification (WSRC)
The discrimination capability of SRC is lost in datasets which distribute in the same direction (18). Distribution of data in the same direction means that the samples with the same vector directions are members of different classes (18). SRC requires normalizing the samples and leads to mapping the samples onto a hypersphere (18). Therefore, data with the same direction distribution are not separable.
Although the mentioned normalization is ineffective for the solution of SRC performance, it is an inseparable section of the SRC algorithm. WSRC remedies the limitations of SRC and its performance improves through adding the weights (19). We proposed using the Minkowski distance between the new sample and the related training samples as weights.

Support Vector Machine (SVM)
SVM is a conventional supervised learning method that has a favorable performance for classification of highdimensional data (23). SVM constructs a hyperplane in classifying the data to maximally separate different groups (24). In our analysis, we used the Statistical Pattern Recognition Toolbox for MATLAB.

Cross-validation
In this study, a leave-one-out cross-validation was performed for evaluating the classification performance of the methods. The function was trained separate times (where is the number of samples) on all the data, except for one sample, in each iteration for which a prediction was made. The average error was calculated to evaluate the performance of methods (25).

Sequential Forward Selection (SFS)
The Sequential Forward Selection (SFS) method is used to assess the overfitting and to select the best combination of parameters for classification of pleural effusions. First, an empty feature subset is considered. Second, a feature providing the best combination with the already selected features is added in from the rest of the features. This process is continued until all the features are selected (26).

Decision tree model
A decision tree is a type of supervised learning algorithm that provides a framework for analyzing all possible alternatives for a decision. This model simplifies decision-making in the presence of uncertainty. The tree starts with a node, a main decision, and the lines extend out from this node for each possible solution. If the solution leads to another decision, the new line extends to the next possible series of choices, which provide an overall supportive decision-making process in medicine (19).

RESULTS
The characterizations of patients and pleural fluid biomarkers for each pleural effusion group are shown in   and specificity, 51.0%).
The SFS algorithm was employed to obtain the best combination of parameters for the classification of pleural effusion. This optimal set of discriminators not only yields high accuracy with the minimum possible number of parameters, but also offers insight into the factors affecting the classification. The final best combination of parameters for discriminating TPE from MPE included age, ADA, polynuclear leukocytes and lymphocytes. Density estimates of these parameters are shown in Figure 1.     However, numerous studies have shown that ADA of pleural fluid, an enzyme produced by macrophages and activated T lymphocytes (28), is a valuable biochemical marker, which has a high sensitivity (87 to 100%) and specificity (81 to 97%) for the diagnosis of TPE (29)(30)(31)(32)(33)(34)(35)(36). In agreement with previously mentioned studies, we found that ADA discriminated well between TPE and MPE, with Among these, supervised machine-learning techniques, in which a training procedure is used to create a classification model for testing, are the most-widely used (39,40). SVM is a conventional supervised learning method that has a favorable performance for classification of highdimensional data (41). However, it has a limitation in dealing with noisy data and, as with other supervised learning methods, is a requirement on many labeled training samples (41). On the other hand, to improve classification robustness in respect of noises, a sparse representation technique has been proposed and has been successfully applied to various classification problems (15)(16)(17). The principal addition of SRC is to represent a new sample using the least number of training samples (15).
Since SRC does not contain separate training and testing stages, as in the supervised learning method, this method has no overfitting problem (17). However, the discrimination capability of SRC is lost in datasets that are distributed in the same direction (18). In this study, the SRC prototype classification method has been modified through adding the weights (WSRC) for solving some of the dataset problems and improving the classification accuracy of the system (19